TrustRadius: an HG Insights company

Save this comparison

Save this comparison

Add Product

Recommended Comparisons

    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    Git

    Score10 out of 10
    N/AN/AN/A

    Optimizely Feature Experimentation

    Score8.4 out of 10
    N/AOptimizely Feature Experimentation unites feature flagging, A/B testing, and built-in collaboration—so marketers can release, experiment, and optimize with confidence in one platform.N/A
    Pricing
    GitOptimizely Feature Experimentation
    Editions & Modules
    No answers on this topic
    No answers on this topic
    Offerings
    Pricing Offerings
    GitOptimizely Feature Experimentation
    Free Trial
    NoNo
    Free/Freemium Version
    NoYes
    Premium Consulting/Integration Services
    NoYes
    Entry-level Setup FeeNo setup feeRequired
    Additional Details——
    More Pricing Information
    Community Pulse
    GitOptimizely Feature Experimentation
    Considered Both Products
    Open Source
    No answer on this topic
    Optimizely
    No answer on this topic
    Key User Insights
    Would buy again
    100%
    Would buy again
    12 Answers
    89%
    Would buy again
    41 Answers
    Delivers good value for the price
    100%
    Delivers good value for the price
    11 Answers
    97%
    Delivers good value for the price
    33 Answers
    Happy with the feature set
    100%
    Happy with the feature set
    12 Answers
    91%
    Happy with the feature set
    42 Answers
    Lived up to sales and marketing promises
    100%
    Lived up to sales and marketing promises
    9 Answers
    88%
    Lived up to sales and marketing promises
    21 Answers
    Implementation went as expected
    100%
    Implementation went as expected
    9 Answers
    80%
    Implementation went as expected
    28 Answers
    Best Alternatives
    GitOptimizely Feature Experimentation
    Small Businesses
    GitHub
    Score9.2 out of 10
    No answers on this topic
    Medium-sized Companies
    GitHub
    Score9.2 out of 10
    No answers on this topic
    Enterprises
    Perforce P4
    Score7.5 out of 10
    No answers on this topic
    All AlternativesView all alternativesView all alternatives
    User Ratings
    GitOptimizely Feature Experimentation
    Likelihood to Recommend
    10.0
    (36 ratings)
    8.2
    (47 ratings)
    Likelihood to Renew
    10.0
    (1 ratings)
    4.5
    (2 ratings)
    Usability
    9.0
    (1 ratings)
    7.6
    (26 ratings)
    Support Rating
    8.5
    (11 ratings)
    3.6
    (1 ratings)
    Implementation Rating
    9.0
    (1 ratings)
    10.0
    (1 ratings)
    Product Scalability
    -
    (0 ratings)
    5.0
    (1 ratings)
    User Testimonials
    GitOptimizely Feature Experimentation
    Likelihood to Recommend
    Open Source
    GIT is good to be used for faster and high availability operations during code release cycle. Git provides a complete replica of the repository on the developer's local system which is why every developer will have complete repository available for quick access on his system and they can merge the specific branches that they have worked on back to the centralized repository. The limitations with GIT are seen when checking in large files.
    Incentivized
    Read full review
    Optimizely
    Based on my experience with Optimizely Feature Experimentation, I can highlight several scenarios where it excels and a few where it may be less suitable. Well-suited scenarios: - Multi-Channel product launches - Complex A/B testing and feature flag management - Gradual rollout and risk mitigation Less suited scenarios: - Simple A/B tests (their Web Experimentation product is probably better for that) - Non-technical team usage -
    Incentivized
    Read full review
    Pros
    Open Source
    • Ability to create branches off current releases to modify code that can be tested in a separate environment.
    • Each developer had their own local copy of branches so it minimizes mistakes being made.
    • Has a user-friendly UI called Git Gui that users can use if they do not like using the command line.
    • Conflicts are displayed nicely so that developers can resolve with ease.
    Incentivized
    Read full review
    Optimizely
    • It is easy to use any of our product owners, marketers, developers can set up experiments and roll them out with some developer support. So the key thing there is this front end UI easy to use and maybe this will come later, but the new features such as Opal and the analytics or database centric engine is something we're interested in as well.
    Incentivized
    Read full review
    Cons
    Open Source
    • There can be quite a number of commands once you get to the advanced features and functionality of Git. Takes time to master.
    • Doesn't handle static assets (ie: videos, images, etc.) well. Although in the recent years, new functionality has been introduced to address this.
    • Many different GUIs, many people (including myself) opt to just use the command-line.
    Incentivized
    Read full review
    Optimizely
    • Would be nice to able to switch variants between say an MVT to a 50:50 if one of the variants is not performing very well quickly and effectively so can still use the standardised report
    • Interface can feel very bare bones/not very many graphs or visuals, which other providers have to make it a bit more engaging
    • Doesn't show easily what each variant that is live looks like, so can be hard to remember what is actually being shown in each test
    Incentivized
    Read full review
    Likelihood to Renew
    Open Source
    Git has met all standards for a source control tool and even exceeded those standards. Git is so integrated with our work that I can't imagine a day without it.
    Incentivized
    Read full review
    Optimizely
    Competitive landscape
    Incentivized
    Read full review
    Usability
    Open Source
    Git is easy to use most of the time. You mostly use a few commands like commiting, fetch/pull, and push which will get you by for most of time.
    Incentivized
    Read full review
    Optimizely
    Easy to navigate the UI. Once you know how to use it, it is very easy to run experiments. And when the experiment is setup, the SDK code variables are generated and available for developers to use immediately so they can quickly build the experiment code
    Incentivized
    Read full review
    Support Rating
    Open Source
    I am not sure what the official Git support channels are like as I have never needed to use any official support. Because Git is so popular among all developers now, it is pretty easy to find the answer to almost any Git question with a quick Google search. I've never had trouble finding what I'm looking for.
    Incentivized
    Read full review
    Optimizely
    Support was there but it was pretty slow at most times. Only after escalation was support really given to our teams
    Incentivized
    Read full review
    Implementation Rating
    Open Source
    It's easy to set up and get going.
    Incentivized
    Read full review
    Optimizely
    It’s straightforward. Docs are well written and I believe there must be a support. But we haven’t used it
    Incentivized
    Read full review
    Alternatives Considered
    Open Source
    I've used both Apache Subversion & Git over the years and have maintained my allegiance to Git. Git is not objectively better than Subversion. It's different.
    The key difference is that it is decentralized. With Subversion, you have a problem here: The SVN Repository may be in a location you can't reach (behind a VPN, intranet - etc), you cannot commit. If you want to make a copy of your code, you have to literally copy/paste it. With Git, you do not have this problem. Your local copy is a repository, and you can commit to it and get all benefits of source control. When you regain connectivity to the main repository, you can commit against it. Another thing for consideration is that Git tracks content rather than files. Branches are lightweight and merging is easy, and I mean really easy.
    It's distributed, basically every repository is a branch. It's much easier to develop concurrently and collaboratively than with Subversion, in my opinion. It also makes offline development possible. It doesn't impose any workflow, as seen on the above linked website, there are many workflows possible with Git. A Subversion-style workflow is easily mimicked.
    Incentivized
    Read full review
    Optimizely
    When Google Optimize goes off we searched for a tool where you can be sure to get a good GA4 implementation and easy to use for IT team and product team. Optimizely Feature Experimentation seems to have a good balance between pricing and capabilities. If you are searching for an experimentation tool and personalization all in one... then maybe these comparison change and Optimizely turns to expensive. In the same way... if you want a server side solution. For us, it will be a challenge in the following years
    Incentivized
    Read full review
    Scalability
    Open Source
    No answers on this topic
    Optimizely
    had troubles with performance for SSR and the React SDK
    Incentivized
    Read full review
    Return on Investment
    Open Source
    • Git has saved our organization countless hours having to manually trace code to a breaking change or manage conflicting changes. It has no equal when it comes to scalability or manageability.
    • Git has allowed our engineering team to build code reviews into its workflow by preventing a developer from approving or merging in their own code; instead, all proposed changes are reviewed by another engineer to assess the impact of the code and whether or not it should be merged in first. This greatly reduces the likelihood of breaking changes getting into production.
    • Git has at times created some confusion among developers about what to do if they accidentally commit a change they decide later they want to roll back. There are multiple ways to address this problem and the best available option may not be obvious in all cases.
    Incentivized
    Read full review
    Optimizely
    • We have improved various metrics throughout the course of our experimentation program with Optimizely and therefore sharing numbers is tricky. Essentially we only implement versions of the product that perform the best in terms of CVR, revenue/visitor, ATV, average order value, average basket size and so forth dependent on the north star we are trying to move with each release.
    Incentivized
    Read full review
    ScreenShots

    Optimizely Feature Experimentation Screenshots

    Screenshot of Feature Flag Setup. Here users can run A/B and multi-armed bandit tests, as well as:

- Set up a single feature flag to test multiple variations and experiment types
- Enable targeted deliveries and rollouts for more precise experimentation
- Roll back changes quickly when needed to ensure experiment accuracy and reduce risks
- Increase testing flexibility with control over experiment types and delivery methodsScreenshot of Audience Setup. This is used to target specific user segments for personalized experiments, and:

- Create and customize audiences based on user attributes
- Refine audience segments to ensure the right users are included in tests
- Enhance experiment relevance by setting specific conditions for user groupsScreenshot of Experiment Results, supporting the analysis and optimization of experimentation outcomes. Viewers can also:

- examine detailed experiment results, including key metrics like conversion rates and statistical significance
- Compare variations side-by-side to identify winning treatments
- Use advanced filters to segment and drill down into specific audience or test dataScreenshot of A Program Overview. These offer insights into any experimentation program’s performance. It also offers:

- A comprehensive view of the entire experimentation program’s status and progress
- Monitoring for key performance metrics like test velocity, success rates, and overall impact
- Evaluation of the impact of experiments with easy-to-read visualizations and reporting tools
- Performance tracking of experiments over time to guide decision-making and optimize strategiesScreenshot of AI Variable Suggestions. These enhance experimentation with AI-driven insights, and can also help with:

- Generating multiple content variations with AI to speed up experiment design
- Improving test quality with content suggestions
- Increasing experimentation velocity and achieving better outcomes with AI-powered optimizationScreenshot of Schedule Changes, to streamline experimentation. Users can also:

- Set specific times to toggle flags or rules on/off, ensuring precise control
- Schedule traffic allocation percentages for smooth experiment rollouts
- Increase test velocity and confidence by automating progressive changes